Handwritten Text Classification Based on Convolutional Neural Network

Abstract

The convolutional neural network (CNN) is a popular and highly effective deep learning technique for image classification. As the popularity of CNNs grew, the model has become popular in several machine learning problems. This paper utilizes a CNN model and the popular LeNet-5 transfer learned model to classify texts after the words are preprocessed and segmented from an image. The EMNIST database is used to train the models. The paper achieves an 89.36% validation accuracy on the EMNIST Balanced dataset and an 86.64% on the EMNIST By_Class dataset for the CNN model of four convolutional layers and one dense layer. Similarly, the LeNet-5 model obtained a validation accuracy of 85.88% on the EMNIST Balanced dataset and 85.01% accuracy on the EMNIST By_Class dataset. However, despite a higher accuracy in the EMNIST Balanced dataset, the EMNIST By_Class dataset achieves better results in real-world handwritten texts.